Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 58732

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 64366

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-13T21:31:58+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`)
beds_by_pref
}

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	117098
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	115361	0
dateAnnounced	0	1.00	10	10	0	290	0
gender	21388	0.82	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	113065	0.03	8	23	0	8	0
notes	61002	0.48	1	270	0	53298	1
mhlwPatientNumber	116649	0.00	1	11	0	434	0
prefecturePatientNumber	18708	0.84	5	20	0	98381	0
prefectureSourceURL	85777	0.27	5	224	0	3442	0
residence	29519	0.75	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8394	0
relatedPatients	106248	0.09	2	259	0	6553	0
knownCluster	114598	0.02	3	88	0	231	0
detectedCityTown	90487	0.23	2	22	0	663	0
cityPrefectureNumber	90744	0.23	1	34	0	26345	2
citySourceURL	105185	0.10	9	317	0	3656	0
deceasedDate	115224	0.02	10	10	0	240	0
deceasedReportedDate	115878	0.01	10	62	0	207	0
deathSourceURL	116023	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 115360, FAL: 1738
charterFlightPassenger	117084	0	1.00	TRU: 14
cruisePassengerDisembarked	117087	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	30.85	24.11	-1	20	30	50	100	▆▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	114026
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	114026	0
knownCluster	111576	0.02	3	88	0	228	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-13	2020-08-19	287
deceasedDate	113648	0	2020-02-13	2020-10-17	2020-05-08	149
deceasedReportedDate	113697	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	19898	0.83	FALSE	2	M: 52778, F: 41350
patientStatus	111516	0.02	FALSE	8	Hos: 1246, Dec: 370, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	20: 25670, -1: 19985, 30: 16313, 40: 13609
pcode	0	1.00	FALSE	47	13: 34182, 27: 14870, 14: 9910, 23: 7400
pref	0	1.00	FALSE	47	東京都: 34182, 大阪府: 14870, 神奈川: 9910, 愛知県: 7400
region	0	1.00	FALSE	8	関東地: 58732, 近畿地: 23314, 九州地: 11525, 中部地: 11471
広域圏	8717	0.92	FALSE	8	首都圏: 59010, 近畿圏: 22705, 中部圏: 10055, 九州圏: 7852
通俗的区分	0	1.00	FALSE	11	関東: 58732, 関西: 22705, 東海: 9627, 九州: 7852
fct_pref	0	1.00	FALSE	47	Tok: 34182, Osa: 14870, Kan: 9910, Aic: 7400

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 114026
charterFlightPassenger	114019	0	1.00	TRU: 7
cruisePassengerDisembarked	114015	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 111576, TRU: 2450

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.9）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.9）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

　

地方別

陽性者数（単日）

　

移動平均

　

累積

　

単日＋累積

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 283.9991 273.1314 306.8862 306.3071 327.1386 356.1355 364.7384 400.2233
##  [9] 404.7158 428.5710 439.6598 462.2164 481.2769 496.5842
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 274.5821 269.5970
## 44.57143 262.7988 257.3291
## 44.71429 293.4796 286.3826
## 44.85714 291.8963 284.2677
## 45.00000 310.9914 302.4436
## 45.14286 337.7382 327.9992
## 45.28571 344.0327 333.0718
## 45.42857 375.4443 362.3271
## 45.57143 377.1847 362.6106
## 45.71429 397.0386 380.3463
## 45.85714 404.9924 386.6406
## 46.00000 424.2233 404.1110
## 46.14286 439.6525 417.6178
## 46.28571 451.0993 427.0211
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 293.4161 298.4012
## 44.57143 283.4640 288.9338
## 44.71429 320.2928 327.3898
## 44.85714 320.7179 328.3465
## 45.00000 343.2858 351.8336
## 45.14286 374.5329 384.2718
## 45.28571 385.4441 396.4050
## 45.42857 425.0023 438.1195
## 45.57143 432.2469 446.8210
## 45.71429 460.1035 476.7957
## 45.85714 474.3273 492.6791
## 46.00000 500.2095 520.3218
## 46.14286 522.9013 544.9359
## 46.28571 542.0691 566.1474
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 29.13070 29.39330 26.29674 31.29827 29.41190 32.07740 30.95173 32.37970
##  [9] 31.71799 32.48659 32.10197 32.51742 32.29579 32.52120
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 22.44864 18.91137
## 44.57143 22.29237 18.53337
## 44.71429 19.08190 15.26259
## 44.85714 24.07919 20.25764
## 45.00000 21.96019 18.01550
## 45.14286 24.52586 20.52831
## 45.28571 23.11952 18.97340
## 45.42857 24.36352 20.12000
## 45.57143 23.41443 19.01879
## 45.71429 23.95745 19.44240
## 45.85714 23.29480 18.63256
## 46.00000 23.47033 18.68109
## 46.14286 22.98429 18.05507
## 46.28571 22.96899 17.91235
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 35.81275 39.35002
## 44.57143 36.49422 40.25322
## 44.71429 33.51159 37.33090
## 44.85714 38.51736 42.33891
## 45.00000 36.86360 40.80829
## 45.14286 39.62895 43.62650
## 45.28571 38.78395 42.93007
## 45.42857 40.39588 44.63939
## 45.57143 40.02155 44.41719
## 45.71429 41.01573 45.53078
## 45.85714 40.90915 45.57139
## 46.00000 41.56452 46.35376
## 46.14286 41.60730 46.53652
## 46.28571 42.07342 47.13006
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 677.5082 530.2773 433.1362 618.7550 646.7569 745.2444 712.5419 714.2972
##  [9] 548.9023 453.4904 618.4411 652.9000 745.2925 726.5287
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 614.2404 580.7484
## 44.57143 450.6407 408.4836
## 44.71429 345.1775 298.6150
## 44.85714 527.7129 479.5180
## 45.00000 552.3504 502.3746
## 45.14286 646.9217 594.8728
## 45.28571 607.4812 551.8655
## 45.42857 596.6924 534.4361
## 45.57143 421.0250 353.3308
## 45.71429 319.2153 248.1343
## 45.85714 479.9121 406.5793
## 46.00000 511.2118 436.2066
## 46.14286 600.1494 523.3153
## 46.28571 577.4100 498.4713
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 740.7761 774.2680
## 44.57143 609.9140 652.0710
## 44.71429 521.0948 567.6573
## 44.85714 709.7972 757.9920
## 45.00000 741.1634 791.1392
## 45.14286 843.5671 895.6159
## 45.28571 817.6025 873.2182
## 45.42857 831.9021 894.1583
## 45.57143 676.7796 744.4738
## 45.71429 587.7655 658.8465
## 45.85714 756.9701 830.3029
## 46.00000 794.5882 869.5934
## 46.14286 890.4356 967.2697
## 46.28571 875.6474 954.5861
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 225.0130 199.7896 185.3999 221.4307 224.4763 228.8373 244.8623 260.0948
##  [9] 234.2106 238.4916 258.1982 262.3192 278.4774 264.3873
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 206.6972 197.0014
## 44.57143 176.9930 164.9251
## 44.71429 158.5522 144.3398
## 44.85714 190.4680 174.0773
## 45.00000 190.8905 173.1112
## 45.14286 191.8775 172.3121
## 45.28571 205.3048 184.3643
## 45.42857 214.8783 190.9421
## 45.57143 184.1542 157.6559
## 45.71429 184.7571 156.3117
## 45.85714 200.2101 169.5131
## 46.00000 200.8957 168.3801
## 46.14286 213.7443 179.4767
## 46.28571 196.1419 160.0149
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 243.3289 253.0247
## 44.57143 222.5863 234.6541
## 44.71429 212.2476 226.4600
## 44.85714 252.3934 268.7840
## 45.00000 258.0622 275.8415
## 45.14286 265.7972 285.3626
## 45.28571 284.4198 305.3603
## 45.42857 305.3113 329.2475
## 45.57143 284.2670 310.7652
## 45.71429 292.2261 320.6714
## 45.85714 316.1863 346.8833
## 46.00000 323.7427 356.2584
## 46.14286 343.2105 377.4781
## 46.28571 332.6327 368.7596
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 405.5378 351.6610 309.0707 422.2897 424.4107 437.0221 472.4557 474.2537
##  [9] 432.5398 387.1714 497.6209 512.3511 517.7590 542.9191
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 373.0086 355.7887
## 44.57143 313.2820 292.9654
## 44.71429 267.7691 245.9053
## 44.85714 376.7209 352.5982
## 45.00000 376.0711 350.4816
## 45.14286 385.2372 357.8240
## 45.28571 418.0560 389.2586
## 45.42857 411.5554 378.3649
## 45.57143 364.9898 329.2310
## 45.71429 315.4818 277.5316
## 45.85714 421.7328 381.5601
## 46.00000 432.6976 390.5316
## 46.14286 434.3515 390.1983
## 46.28571 456.0387 410.0470
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 438.0671 455.2870
## 44.57143 390.0399 410.3565
## 44.71429 350.3723 372.2360
## 44.85714 467.8586 491.9813
## 45.00000 472.7503 498.3398
## 45.14286 488.8069 516.2202
## 45.28571 526.8554 555.6529
## 45.42857 536.9521 570.1425
## 45.57143 500.0898 535.8485
## 45.71429 458.8610 496.8112
## 45.85714 573.5090 613.6817
## 46.00000 592.0046 634.1706
## 46.14286 601.1665 645.3198
## 46.28571 629.7995 675.7912
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 16.94054 15.07259 15.07259 15.07259 15.07259 15.07259 15.07259 15.07259
##  [9] 15.07259 15.07259 15.07259 15.07259 15.07259 15.07259
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%          95%
## 44.42857 7.860999  3.054580884
## 44.57143 5.593197  0.575109197
## 44.71429 5.406204  0.289127171
## 44.85714 5.222759  0.008573378
## 45.00000 5.042670 -0.266850027
## 45.14286 4.865757 -0.537414611
## 45.28571 4.691859 -0.803368791
## 45.42857 4.520826 -1.064940516
## 45.57143 4.352522 -1.322339544
## 45.71429 4.186820 -1.575759419
## 45.85714 4.023602 -1.825379168
## 46.00000 3.862761 -2.071364784
## 46.14286 3.704195 -2.313870516
## 46.28571 3.547810 -2.553040001
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 26.02008 30.82649
## 44.57143 24.55198 29.57007
## 44.71429 24.73898 29.85605
## 44.85714 24.92242 30.13661
## 45.00000 25.10251 30.41203
## 45.14286 25.27942 30.68260
## 45.28571 25.45332 30.94855
## 45.42857 25.62436 31.21012
## 45.57143 25.79266 31.46752
## 45.71429 25.95836 31.72094
## 45.85714 26.12158 31.97056
## 46.00000 26.28242 32.21655
## 46.14286 26.44099 32.45905
## 46.28571 26.59737 32.69822
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 2.111738 2.405062 2.405062 2.405062 2.405062 2.405062 2.405062 2.405062
##  [9] 2.405062 2.405062 2.405062 2.405062 2.405062 2.405062
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.370970 -3.214605
## 44.57143 -1.326800 -3.302328
## 44.71429 -1.398898 -3.412592
## 44.85714 -1.469654 -3.520805
## 45.00000 -1.539142 -3.627078
## 45.14286 -1.607427 -3.731510
## 45.28571 -1.674568 -3.834194
## 45.42857 -1.740623 -3.935216
## 45.57143 -1.805641 -4.034653
## 45.71429 -1.869671 -4.132577
## 45.85714 -1.932755 -4.229057
## 46.00000 -1.994935 -4.324153
## 46.14286 -2.056249 -4.417924
## 46.28571 -2.116731 -4.510424
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 5.594447 7.438081
## 44.57143 6.136923 8.112452
## 44.71429 6.209022 8.222716
## 44.85714 6.279778 8.330929
## 45.00000 6.349266 8.437202
## 45.14286 6.417551 8.541634
## 45.28571 6.484692 8.644318
## 45.42857 6.550747 8.745340
## 45.57143 6.615765 8.844777
## 45.71429 6.679795 8.942701
## 45.85714 6.742879 9.039181
## 46.00000 6.805059 9.134277
## 46.14286 6.866373 9.228048
## 46.28571 6.926855 9.320548
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 64.45245 73.24540 57.80116 64.78839 77.40435 71.87055 83.78668 72.75355
##  [9] 78.18562 70.69663 75.73243 81.60504 77.72972 87.02141
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%        95%
## 44.42857 42.43973  30.786902
## 44.57143 46.54033  32.403515
## 44.71429 27.62966  11.657830
## 44.85714 33.27186  16.588009
## 45.00000 43.82528  26.049597
## 45.14286 34.38624  14.543254
## 45.28571 43.70018  22.479676
## 45.42857 27.15904   3.022772
## 45.57143 28.70360   2.509400
## 45.71429 16.89975 -11.578605
## 45.85714 18.76340 -11.394183
## 46.00000 21.83574  -9.804208
## 46.14286 14.64453 -18.750757
## 46.28571 20.75213 -14.328713
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857  86.46518  98.11801
## 44.57143  99.95048 114.08729
## 44.71429  87.97265 103.94448
## 44.85714  96.30493 112.98877
## 45.00000 110.98342 128.75911
## 45.14286 109.35485 129.19784
## 45.28571 123.87317 145.09368
## 45.42857 118.34805 142.48432
## 45.57143 127.66765 153.86185
## 45.71429 124.49352 152.97187
## 45.85714 132.70146 162.85905
## 46.00000 141.37433 173.01428
## 46.14286 140.81492 174.21020
## 46.28571 153.29069 188.37153

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 283.9991 273.1314 306.8862 306.3071 327.1386 356.1355 364.7384 400.2233
##  [9] 404.7158 428.5710 439.6598 462.2164 481.2769 496.5842
## 
## $北海道$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 274.5821 269.5970
## 44.57143 262.7988 257.3291
## 44.71429 293.4796 286.3826
## 44.85714 291.8963 284.2677
## 45.00000 310.9914 302.4436
## 45.14286 337.7382 327.9992
## 45.28571 344.0327 333.0718
## 45.42857 375.4443 362.3271
## 45.57143 377.1847 362.6106
## 45.71429 397.0386 380.3463
## 45.85714 404.9924 386.6406
## 46.00000 424.2233 404.1110
## 46.14286 439.6525 417.6178
## 46.28571 451.0993 427.0211
## 
## $北海道$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 293.4161 298.4012
## 44.57143 283.4640 288.9338
## 44.71429 320.2928 327.3898
## 44.85714 320.7179 328.3465
## 45.00000 343.2858 351.8336
## 45.14286 374.5329 384.2718
## 45.28571 385.4441 396.4050
## 45.42857 425.0023 438.1195
## 45.57143 432.2469 446.8210
## 45.71429 460.1035 476.7957
## 45.85714 474.3273 492.6791
## 46.00000 500.2095 520.3218
## 46.14286 522.9013 544.9359
## 46.28571 542.0691 566.1474
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] -0.05756113  0.87274770  0.30106972  0.65236781  0.43649392  0.56914917
##  [7]  0.48763206  0.53772461  0.50694257  0.52585824  0.51423449  0.52137732
## [13]  0.51698803  0.51968526
## 
## $青森県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -2.602689 -3.949998
## 44.57143 -2.124287 -3.710822
## 44.71429 -2.776993 -4.406421
## 44.85714 -2.667185 -4.424450
## 45.00000 -3.005609 -4.827748
## 45.14286 -3.048955 -4.964264
## 45.28571 -3.263235 -5.248824
## 45.42857 -3.361762 -5.426025
## 45.57143 -3.523451 -5.657012
## 45.71429 -3.638562 -5.843073
## 45.85714 -3.775696 -6.046647
## 46.00000 -3.893076 -6.229947
## 46.14286 -4.017024 -6.417184
## 46.28571 -4.131738 -6.594053
## 
## $青森県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.487566 3.834875
## 44.57143 3.869783 5.456317
## 44.71429 3.379132 5.008560
## 44.85714 3.971921 5.729186
## 45.00000 3.878597 5.700736
## 45.14286 4.187254 6.102562
## 45.28571 4.238499 6.224088
## 45.42857 4.437211 6.501474
## 45.57143 4.537336 6.670897
## 45.71429 4.690279 6.894790
## 45.85714 4.804165 7.075116
## 46.00000 4.935831 7.272701
## 46.14286 5.051000 7.451160
## 46.28571 5.171109 7.633423
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 5.325049 5.144661 5.213576 5.289367 6.088240 6.343544 5.811045 5.740768
##  [9] 5.717468 5.709743 5.707182 5.706333 5.706051 5.705958
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 4.388043 3.892022
## 44.57143 4.056937 3.481131
## 44.71429 4.045805 3.427624
## 44.85714 4.060507 3.409988
## 45.00000 4.805285 4.126130
## 45.14286 5.009960 4.304003
## 45.28571 4.429091 3.697528
## 45.42857 4.276994 3.502119
## 45.57143 4.191034 3.382989
## 45.71429 4.127536 3.289967
## 45.85714 4.072484 3.207127
## 46.00000 4.021217 3.129171
## 46.14286 3.972125 3.054241
## 46.28571 3.924604 2.981613
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 6.262054 6.758075
## 44.57143 6.232385 6.808192
## 44.71429 6.381348 6.999528
## 44.85714 6.518226 7.168745
## 45.00000 7.371195 8.050350
## 45.14286 7.677128 8.383084
## 45.28571 7.193000 7.924563
## 45.42857 7.204543 7.979418
## 45.57143 7.243902 8.051947
## 45.71429 7.291950 8.129519
## 45.85714 7.341880 8.207237
## 46.00000 7.391448 8.283494
## 46.14286 7.439977 8.357862
## 46.28571 7.487311 8.430303
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 20.52430 24.69924 21.43471 23.98736 21.99135 23.55210 22.33169 23.28597
##  [9] 22.53979 23.12326 22.66702 23.02377 22.74482 22.96294
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 15.76195 13.24092
## 44.57143 19.88109 17.33052
## 44.71429 16.22754 13.47103
## 44.85714 18.68460 15.87749
## 45.00000 16.40326 13.44511
## 45.14286 17.84322 14.82112
## 45.28571 16.39381 13.25049
## 45.42857 17.21389 13.99953
## 45.57143 16.27204 12.95410
## 45.71429 16.71563 13.32364
## 45.85714 16.08494 12.60060
## 46.00000 16.30074 12.74178
## 46.14286 15.86172 12.21803
## 46.28571 15.94038 12.22286
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 25.28665 27.80769
## 44.57143 29.51738 32.06795
## 44.71429 26.64187 29.39838
## 44.85714 29.29012 32.09723
## 45.00000 27.57943 30.53759
## 45.14286 29.26098 32.28308
## 45.28571 28.26957 31.41290
## 45.42857 29.35806 32.57242
## 45.57143 28.80753 32.12548
## 45.71429 29.53088 32.92287
## 45.85714 29.24910 32.73344
## 46.00000 29.74680 33.30576
## 46.14286 29.62791 33.27160
## 46.28571 29.98550 33.70302
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.3450993 0.4000788 0.4000788 0.4000788 0.4000788 0.4000788 0.4000788
##  [8] 0.4000788 0.4000788 0.4000788 0.4000788 0.4000788 0.4000788 0.4000788
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -0.8887188 -1.541863
## 44.57143 -0.8461026 -1.505791
## 44.71429 -0.8475247 -1.507966
## 44.85714 -0.8489451 -1.510138
## 45.00000 -0.8503640 -1.512308
## 45.14286 -0.8517812 -1.514476
## 45.28571 -0.8531968 -1.516641
## 45.42857 -0.8546108 -1.518803
## 45.57143 -0.8560232 -1.520963
## 45.71429 -0.8574341 -1.523121
## 45.85714 -0.8588433 -1.525276
## 46.00000 -0.8602510 -1.527429
## 46.14286 -0.8616571 -1.529580
## 46.28571 -0.8630617 -1.531728
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.578917 2.232061
## 44.57143 1.646260 2.305949
## 44.71429 1.647682 2.308124
## 44.85714 1.649103 2.310296
## 45.00000 1.650521 2.312466
## 45.14286 1.651939 2.314633
## 45.28571 1.653354 2.316798
## 45.42857 1.654768 2.318961
## 45.57143 1.656181 2.321121
## 45.71429 1.657592 2.323279
## 45.85714 1.659001 2.325434
## 46.00000 1.660409 2.327587
## 46.14286 1.661815 2.329737
## 46.28571 1.663219 2.331885
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 1.4328843 1.2879885 1.3003512 1.3212990 0.9030054 1.0693777 1.4272413
##  [8] 1.0823819 1.0052179 0.9274950 0.8506772 0.7759972 0.7044687 0.6368999
## 
## $山形県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                  80%         95%
## 44.42857  0.48487447 -0.01697159
## 44.57143  0.32053410 -0.19160533
## 44.71429  0.30449408 -0.22268082
## 44.85714  0.28981547 -0.25621894
## 45.00000 -0.16895431 -0.73641548
## 45.14286 -0.04546968 -0.63563423
## 45.28571  0.26925215 -0.34375028
## 45.42857 -0.08215138 -0.69861805
## 45.57143 -0.19007557 -0.82282572
## 45.71429 -0.29381862 -0.94034298
## 45.85714 -0.39217546 -1.05010189
## 46.00000 -0.48428981 -1.15144546
## 46.14286 -0.56959711 -1.24404679
## 46.28571 -0.64777395 -1.32783921
## 
## $山形県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.380894 2.882740
## 44.57143 2.255443 2.767582
## 44.71429 2.296208 2.823383
## 44.85714 2.352783 2.898817
## 45.00000 1.974965 2.542426
## 45.14286 2.184225 2.774390
## 45.28571 2.585230 3.198233
## 45.42857 2.246915 2.863382
## 45.57143 2.200511 2.833262
## 45.71429 2.148809 2.795333
## 45.85714 2.093530 2.751456
## 46.00000 2.036284 2.703440
## 46.14286 1.978534 2.652984
## 46.28571 1.921574 2.601639
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 3.059334 3.059334 3.059334 3.059334 3.059334 3.059334 3.059334 3.059334
##  [9] 3.059334 3.059334 3.059334 3.059334 3.059334 3.059334
## 
## $福島県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                  80%        95%
## 44.42857  0.50822384 -0.8422523
## 44.57143  0.44501273 -0.9389253
## 44.71429  0.38329433 -1.0333155
## 44.85714  0.32296762 -1.1255772
## 45.00000  0.26394251 -1.2158483
## 45.14286  0.20613821 -1.3042524
## 45.28571  0.14948197 -1.3909006
## 45.42857  0.09390798 -1.4758937
## 45.57143  0.03935650 -1.5593230
## 45.71429 -0.01422692 -1.6412718
## 45.85714 -0.06689205 -1.7218161
## 46.00000 -0.11868456 -1.8010259
## 46.14286 -0.16964643 -1.8789654
## 46.28571 -0.21981638 -1.9556937
## 
## $福島県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 5.610444 6.960920
## 44.57143 5.673655 7.057593
## 44.71429 5.735374 7.151983
## 44.85714 5.795700 7.244245
## 45.00000 5.854725 7.334516
## 45.14286 5.912530 7.422920
## 45.28571 5.969186 7.509569
## 45.42857 6.024760 7.594562
## 45.57143 6.079311 7.677991
## 45.71429 6.132895 7.759940
## 45.85714 6.185560 7.840484
## 46.00000 6.237353 7.919694
## 46.14286 6.288314 7.997633
## 46.28571 6.338484 8.074362
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 21.58759 21.66754 21.13901 21.31795 22.73095 23.88944 23.66560 23.41593
##  [9] 23.62242 23.14605 23.35090 24.30226 24.78601 24.81651
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 17.89604 15.94186
## 44.57143 17.70147 15.60196
## 44.71429 16.91622 14.68082
## 44.85714 16.85319 14.48969
## 45.00000 18.03668 15.55167
## 45.14286 18.97636 16.37552
## 45.28571 18.54304 15.83132
## 45.42857 17.95191 15.05943
## 45.57143 17.92126 14.90325
## 45.71429 17.21722 14.07869
## 45.85714 17.20283 13.94824
## 46.00000 17.94251 14.57586
## 46.14286 18.22139 14.74629
## 46.28571 18.05324 14.47297
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 25.27913 27.23332
## 44.57143 25.63360 27.73311
## 44.71429 25.36179 27.59719
## 44.85714 25.78271 28.14621
## 45.00000 27.42523 29.91023
## 45.14286 28.80253 31.40336
## 45.28571 28.78816 31.49988
## 45.42857 28.87995 31.77243
## 45.57143 29.32358 32.34160
## 45.71429 29.07487 32.21340
## 45.85714 29.49896 32.75355
## 46.00000 30.66201 34.02866
## 46.14286 31.35062 34.82572
## 46.28571 31.57979 35.16005
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 1.923568 1.627837 1.775304 2.319689 1.977646 1.978538 1.946906 1.929551
##  [9] 1.929551 1.929551 1.929551 1.929551 1.929551 1.929551
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -1.0545266 -2.631035
## 44.57143 -1.3930408 -2.992197
## 44.71429 -1.2877597 -2.909248
## 44.85714 -0.7849875 -2.428504
## 45.00000 -1.1680921 -2.833345
## 45.14286 -1.2077336 -2.894444
## 45.28571 -1.2793886 -2.987286
## 45.42857 -1.4687437 -3.267692
## 45.57143 -1.5216559 -3.348614
## 45.71429 -1.5737690 -3.428314
## 45.85714 -1.6251182 -3.506846
## 46.00000 -1.6757361 -3.584260
## 46.14286 -1.7256531 -3.660601
## 46.28571 -1.7748976 -3.735914
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 4.901663 6.478172
## 44.57143 4.648715 6.247872
## 44.71429 4.838368 6.459856
## 44.85714 5.424365 7.067881
## 45.00000 5.123384 6.788638
## 45.14286 5.164809 6.851519
## 45.28571 5.173201 6.881098
## 45.42857 5.327845 7.126794
## 45.57143 5.380758 7.207716
## 45.71429 5.432871 7.287416
## 45.85714 5.484220 7.365948
## 46.00000 5.534838 7.443361
## 46.14286 5.584755 7.519703
## 46.28571 5.633999 7.595016
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 7.674706 6.241344 5.945101 7.082717 6.492165 6.540132 6.617742 6.760301
##  [9] 6.375046 6.458567 6.352183 6.433453 6.280624 6.342578
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                  80%       95%
## 44.42857  3.20421900  0.837686
## 44.57143  1.12074202 -1.589941
## 44.71429  0.62447129 -2.192100
## 44.85714  1.69873398 -1.151374
## 45.00000  0.79186030 -2.225699
## 45.14286  0.74452046 -2.323491
## 45.28571  0.71207470 -2.414197
## 45.42857  0.74701209 -2.436231
## 45.57143  0.22127433 -3.036336
## 45.71429  0.25497862 -3.029003
## 45.85714  0.08357430 -3.234827
## 46.00000  0.12307445 -3.217438
## 46.14286 -0.08444789 -3.453913
## 46.28571 -0.05218537 -3.437369
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 12.14519 14.51173
## 44.57143 11.36195 14.07263
## 44.71429 11.26573 14.08230
## 44.85714 12.46670 15.31681
## 45.00000 12.19247 15.21003
## 45.14286 12.33574 15.40375
## 45.28571 12.52341 15.64968
## 45.42857 12.77359 15.95683
## 45.57143 12.52882 15.78643
## 45.71429 12.66216 15.94614
## 45.85714 12.62079 15.93919
## 46.00000 12.74383 16.08434
## 46.14286 12.64570 16.01516
## 46.28571 12.73734 16.12253
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1]  81.25903  70.83641  71.33600  77.00940  95.41427  82.48621  98.74788
##  [8]  89.89944  81.23745  81.71458  86.52087 101.98017  91.22176 104.88775
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 67.61719 60.39563
## 44.57143 56.53725 48.96774
## 44.71429 56.40844 48.50627
## 44.85714 61.47885 53.25747
## 45.00000 79.30328 70.77463
## 45.14286 65.81497 56.98975
## 45.28571 81.53463 72.42248
## 45.42857 70.90236 60.84592
## 45.57143 61.47018 51.00602
## 45.71429 61.20602 50.34945
## 45.85714 65.29690 54.06161
## 46.00000 80.06413 68.46248
## 46.14286 68.63484 56.67805
## 46.28571 81.64931 69.34763
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857  94.90087 102.12242
## 44.57143  85.13557  92.70508
## 44.71429  86.26355  94.16573
## 44.85714  92.53995 100.76133
## 45.00000 111.52526 120.05391
## 45.14286  99.15745 107.98267
## 45.28571 115.96114 125.07329
## 45.42857 108.89652 118.95297
## 45.57143 101.00472 111.46889
## 45.71429 102.22314 113.07971
## 45.85714 107.74484 118.98013
## 46.00000 123.89621 135.49786
## 46.14286 113.80868 125.76547
## 46.28571 128.12618 140.42787
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 60.61562 55.68137 53.10727 57.21014 60.58944 64.35516 60.53324 61.69217
##  [9] 59.97723 58.74911 60.03770 62.29165 62.55101 61.48326
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 49.09936 43.00302
## 44.57143 42.79683 35.97617
## 44.71429 39.80322 32.76048
## 44.85714 43.49941 36.24138
## 45.00000 46.48374 39.01663
## 45.14286 49.86526 42.19476
## 45.28571 45.66905 37.80043
## 45.42857 45.70097 37.23574
## 45.57143 43.33472 34.52470
## 45.71429 41.61903 32.55092
## 45.85714 42.43356 33.11449
## 46.00000 44.22588 34.66244
## 46.14286 44.03512 34.23340
## 46.28571 42.52793 32.49359
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 72.13189 78.22823
## 44.57143 68.56590 75.38656
## 44.71429 66.41132 73.45405
## 44.85714 70.92088 78.17890
## 45.00000 74.69515 82.16225
## 45.14286 78.84507 86.51556
## 45.28571 75.39742 83.26604
## 45.42857 77.68338 86.14861
## 45.57143 76.61974 85.42976
## 45.71429 75.87918 84.94730
## 45.85714 77.64184 86.96091
## 46.00000 80.35741 89.92085
## 46.14286 81.06690 90.86862
## 46.28571 80.43859 90.47293
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 384.3825 276.5283 235.0388 336.1329 321.6623 404.9568 376.6495 399.2269
##  [9] 294.5299 252.4752 353.0218 338.0209 420.8017 391.9969
## 
## $東京都$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 338.8219 314.7036
## 44.57143 223.7934 195.8772
## 44.71429 178.9989 149.3331
## 44.85714 277.1604 245.9423
## 45.00000 260.0654 227.4579
## 45.14286 340.9955 307.1364
## 45.28571 310.5468 275.5542
## 45.42857 325.9172 287.1093
## 45.57143 217.1675 176.2143
## 45.71429 172.1459 129.6221
## 45.85714 270.0054 226.0592
## 46.00000 252.5602 207.3201
## 46.14286 333.1099 286.6886
## 46.28571 302.2622 254.7595
## 
## $東京都$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 429.9431 454.0614
## 44.57143 329.2631 357.1793
## 44.71429 291.0787 320.7444
## 44.85714 395.1053 426.3235
## 45.00000 383.2592 415.8666
## 45.14286 468.9181 502.7772
## 45.28571 442.7522 477.7449
## 45.42857 472.5366 511.3445
## 45.57143 371.8924 412.8455
## 45.71429 332.8045 375.3282
## 45.85714 436.0382 479.9844
## 46.00000 423.4815 468.7216
## 46.14286 508.4936 554.9149
## 46.28571 481.7317 529.2344
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 140.61929 107.90768  88.44736 112.03893 126.69647 132.45126 133.23526
##  [8] 130.45691 115.76862 109.39054 117.51493 126.13845 123.24752 123.61949
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 120.43854 109.75549
## 44.57143  85.27553  73.29479
## 44.71429  64.93621  52.49016
## 44.85714  88.03139  75.32257
## 45.00000 102.31270  89.40472
## 45.14286 107.73559  94.65190
## 45.28571 108.20640  94.95692
## 45.42857 102.37413  87.50801
## 45.57143  86.46287  70.94933
## 45.71429  79.33361  63.42244
## 45.85714  86.86214  70.63554
## 46.00000  94.94995  78.43975
## 46.14286  91.55050  74.77111
## 46.28571  91.42887  74.38819
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 160.8000 171.4831
## 44.57143 130.5398 142.5206
## 44.71429 111.9585 124.4046
## 44.85714 136.0465 148.7553
## 45.00000 151.0802 163.9882
## 45.14286 157.1669 170.2506
## 45.28571 158.2641 171.5136
## 45.42857 158.5397 173.4058
## 45.57143 145.0744 160.5879
## 45.71429 139.4475 155.3586
## 45.85714 148.1677 164.3943
## 46.00000 157.3270 173.8372
## 46.14286 154.9445 171.7239
## 46.28571 155.8101 172.8508
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 5.075986 5.609221 5.190208 5.085746 6.548215 5.482915 5.074833 5.386265
##  [9] 5.386265 5.386265 5.386265 5.386265 5.386265 5.386265
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 3.282859 2.333636
## 44.57143 3.758767 2.779195
## 44.71429 3.284148 2.275141
## 44.85714 3.125658 2.088051
## 45.00000 4.535549 3.470108
## 45.14286 3.419011 2.326445
## 45.28571 2.960931 1.841899
## 45.42857 3.176340 2.006476
## 45.57143 3.119424 1.919431
## 45.71429 3.063902 1.834518
## 45.85714 3.009678 1.751588
## 46.00000 2.956663 1.670509
## 46.14286 2.904780 1.591161
## 46.28571 2.853960 1.513439
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 6.869112 7.818336
## 44.57143 7.459676 8.439248
## 44.71429 7.096267 8.105274
## 44.85714 7.045833 8.083441
## 45.00000 8.560880 9.626321
## 45.14286 7.546820 8.639385
## 45.28571 7.188736 8.307768
## 45.42857 7.596189 8.766053
## 45.57143 7.653105 8.853098
## 45.71429 7.708627 8.938011
## 45.85714 7.762852 9.020941
## 46.00000 7.815867 9.102020
## 46.14286 7.867749 9.181368
## 46.28571 7.918569 9.259090
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.17756714 0.06037413 0.06649649 0.05971932 0.07635326 0.06018543
##  [7] 0.05926176 0.05557096 0.05507476 0.05096796 0.04883267 0.04632809
## [13] 0.04433882 0.04202653
## 
## $富山県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -2.284870 -3.588406
## 44.57143 -2.540507 -3.917330
## 44.71429 -2.693022 -4.153822
## 44.85714 -2.821391 -4.346559
## 45.00000 -2.993165 -4.618069
## 45.14286 -3.120781 -4.804683
## 45.28571 -3.226151 -4.965344
## 45.42857 -3.319014 -5.105411
## 45.57143 -3.404860 -5.236438
## 45.71429 -3.480423 -5.349828
## 45.85714 -3.546902 -5.450370
## 46.00000 -3.606351 -5.539963
## 46.14286 -3.659732 -5.620548
## 46.28571 -3.707614 -5.692554
## 
## $富山県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.640005 3.943540
## 44.57143 2.661255 4.038078
## 44.71429 2.826015 4.286815
## 44.85714 2.940830 4.465998
## 45.00000 3.145871 4.770776
## 45.14286 3.241152 4.925054
## 45.28571 3.344675 5.083867
## 45.42857 3.430155 5.216553
## 45.57143 3.515009 5.346588
## 45.71429 3.582359 5.451764
## 45.85714 3.644568 5.548035
## 46.00000 3.699008 5.632619
## 46.14286 3.748409 5.709226
## 46.28571 3.791667 5.776607
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 1.035537 1.035537 1.035537 1.035537 1.035537 1.035537 1.035537 1.035537
##  [9] 1.035537 1.035537 1.035537 1.035537 1.035537 1.035537
## 
## $石川県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -2.652671 -4.605091
## 44.57143 -2.859976 -4.922137
## 44.71429 -3.056793 -5.223142
## 44.85714 -3.244570 -5.510321
## 45.00000 -3.424447 -5.785420
## 45.14286 -3.597346 -6.049845
## 45.28571 -3.764020 -6.304752
## 45.42857 -3.925097 -6.551098
## 45.57143 -4.081106 -6.789693
## 45.71429 -4.232497 -7.021225
## 45.85714 -4.379657 -7.246287
## 46.00000 -4.522922 -7.465392
## 46.14286 -4.662586 -7.678991
## 46.28571 -4.798909 -7.887477
## 
## $石川県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 4.723745 6.676165
## 44.57143 4.931050 6.993211
## 44.71429 5.127867 7.294216
## 44.85714 5.315644 7.581395
## 45.00000 5.495521 7.856494
## 45.14286 5.668420 8.120920
## 45.28571 5.835094 8.375826
## 45.42857 5.996171 8.622172
## 45.57143 6.152180 8.860767
## 45.71429 6.303571 9.092299
## 45.85714 6.450731 9.317361
## 46.00000 6.593996 9.536466
## 46.14286 6.733660 9.750065
## 46.28571 6.869983 9.958551
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.9685740 1.2968081 1.4605302 0.9706781 1.2426970 1.1766381 0.9892592
##  [8] 1.1394897 1.0398840 0.9880901 1.0493638 0.9758781 0.9716201 0.9864203
## 
## $福井県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -0.8643720 -1.834675
## 44.57143 -0.6457668 -1.674104
## 44.71429 -0.6836906 -1.818773
## 44.85714 -1.4168880 -2.680789
## 45.00000 -1.1890268 -2.476304
## 45.14286 -1.3550634 -2.695265
## 45.28571 -1.6137109 -2.991640
## 45.42857 -1.4883423 -2.879433
## 45.57143 -1.6338051 -3.049171
## 45.71429 -1.7101232 -3.138471
## 45.85714 -1.6632727 -3.099256
## 46.00000 -1.7563747 -3.202742
## 46.14286 -1.7703266 -3.221826
## 46.28571 -1.7634042 -3.219073
## 
## $福井県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.801520 3.771823
## 44.57143 3.239383 4.267720
## 44.71429 3.604751 4.739833
## 44.85714 3.358244 4.622145
## 45.00000 3.674421 4.961698
## 45.14286 3.708340 5.048542
## 45.28571 3.592229 4.970159
## 45.42857 3.767322 5.158412
## 45.57143 3.713573 5.128939
## 45.71429 3.686303 5.114651
## 45.85714 3.762000 5.197984
## 46.00000 3.708131 5.154498
## 46.14286 3.713567 5.165066
## 46.28571 3.736245 5.191914
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 5.084607 5.603355 5.732445 5.764568 5.772562 5.774551 5.775046 5.775169
##  [9] 5.775200 5.775208 5.775209 5.775210 5.775210 5.775210
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 3.332716 2.405320
## 44.57143 3.682787 2.666100
## 44.71429 3.735509 2.678396
## 44.85714 3.709056 2.620934
## 45.00000 3.663477 2.546995
## 45.14286 3.614026 2.470313
## 45.28571 3.564472 2.394265
## 45.42857 3.515703 2.319613
## 45.57143 3.467888 2.246470
## 45.71429 3.421025 2.174796
## 45.85714 3.375072 2.104516
## 46.00000 3.329981 2.035556
## 46.14286 3.285707 1.967844
## 46.28571 3.242207 1.901315
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 6.836498 7.763893
## 44.57143 7.523924 8.540611
## 44.71429 7.729380 8.786493
## 44.85714 7.820080 8.908202
## 45.00000 7.881647 8.998129
## 45.14286 7.935076 9.078789
## 45.28571 7.985620 9.155827
## 45.42857 8.034636 9.230725
## 45.57143 8.082512 9.303929
## 45.71429 8.129390 9.375619
## 45.85714 8.175347 9.445903
## 46.00000 8.220438 9.514864
## 46.14286 8.264713 9.582576
## 46.28571 8.308214 9.649105
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 12.45376 15.12620 12.73584 14.87389 12.96152 14.67203 13.14207 14.51054
##  [9] 13.28652 14.38134 13.40208 14.27797 13.49453 14.19528
## 
## $長野県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857  9.481588  7.908217
## 44.57143 12.019119 10.374332
## 44.71429  9.387320  7.614719
## 44.85714 11.395438  9.554058
## 45.00000  9.276618  7.325949
## 45.14286 10.861402  8.844178
## 45.28571  9.149896  7.036566
## 45.42857 10.396494  8.218652
## 45.57143  9.009481  6.745355
## 45.71429  9.986101  7.659403
## 45.85714  8.858149  6.452738
## 46.00000  9.619367  7.153250
## 46.14286  8.698555  6.159718
## 46.28571  9.288016  6.690269
## 
## $長野県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 15.42592 16.99930
## 44.57143 18.23327 19.87806
## 44.71429 16.08436 17.85697
## 44.85714 18.35233 20.19371
## 45.00000 16.64642 18.59709
## 45.14286 18.48266 20.49988
## 45.28571 17.13425 19.24758
## 45.42857 18.62458 20.80242
## 45.57143 17.56355 19.82768
## 45.71429 18.77657 21.10327
## 45.85714 17.94601 20.35142
## 46.00000 18.93658 21.40269
## 46.14286 18.29051 20.82935
## 46.28571 19.10254 21.70029
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 10.42216 10.19544 10.95379 12.32987 11.34622 14.19947 12.33186 11.20139
##  [9] 11.54149 11.55137 12.36904 12.07108 13.11999 11.70154
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 7.050554 5.265736
## 44.57143 6.389437 4.374660
## 44.71429 6.771647 4.557756
## 44.85714 7.900296 5.555419
## 45.00000 6.713957 4.261784
## 45.14286 9.395543 6.852497
## 45.28571 7.374017 4.749491
## 45.42857 5.832025 2.989653
## 45.57143 5.930089 2.959594
## 45.71429 5.713526 2.623158
## 45.85714 6.333387 3.138305
## 46.00000 5.851554 2.559136
## 46.14286 6.727478 3.343485
## 46.28571 5.143697 1.672185
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 13.79376 15.57858
## 44.57143 14.00144 16.01622
## 44.71429 15.13593 17.34982
## 44.85714 16.75945 19.10433
## 45.00000 15.97849 18.43066
## 45.14286 19.00340 21.54645
## 45.28571 17.28971 19.91424
## 45.42857 16.57076 19.41313
## 45.57143 17.15289 20.12338
## 45.71429 17.38921 20.47958
## 45.85714 18.40469 21.59977
## 46.00000 18.29060 21.58302
## 46.14286 19.51251 22.89650
## 46.28571 18.25938 21.73089
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 17.00761 14.95849 13.17610 17.50854 15.15496 15.21893 17.46119 19.69951
##  [9] 15.92931 15.17815 16.96813 17.11472 16.00602 16.90974
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 13.151829 11.110699
## 44.57143 10.495719  8.133272
## 44.71429  8.469434  5.977877
## 44.85714 12.646082 10.072051
## 45.00000  9.918215  7.146048
## 45.14286  9.738027  6.836612
## 45.28571 11.817574  8.830022
## 45.42857 13.828790 10.721016
## 45.57143  9.853015  6.636419
## 45.71429  8.946821  5.648158
## 45.85714 10.528099  7.118951
## 46.00000 10.483531  6.973193
## 46.14286  9.219893  5.627534
## 46.28571  9.943613  6.255968
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 20.86340 22.90453
## 44.57143 19.42126 21.78370
## 44.71429 17.88276 20.37432
## 44.85714 22.37100 24.94503
## 45.00000 20.39171 23.16388
## 45.14286 20.69983 23.60125
## 45.28571 23.10481 26.09237
## 45.42857 25.57024 28.67801
## 45.57143 22.00560 25.22220
## 45.71429 21.40947 24.70813
## 45.85714 23.40817 26.81732
## 46.00000 23.74590 27.25624
## 46.14286 22.79215 26.38451
## 46.28571 23.87587 27.56351
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 151.4955 139.1030 124.1865 141.7378 140.1498 160.4008 165.2488 164.8818
##  [9] 157.4197 148.0129 143.7250 147.4961 156.1551 163.0557
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%       95%
## 44.42857 137.0712 129.43543
## 44.57143 121.8560 112.72598
## 44.71429 104.1827  93.59337
## 44.85714 119.4674 107.67815
## 45.00000 116.2318 103.57042
## 45.14286 135.2552 121.94389
## 45.28571 138.9768 125.06920
## 45.42857 135.2332 119.53821
## 45.57143 125.0798 107.96009
## 45.71429 112.8816  94.28428
## 45.85714 106.1679  86.28638
## 46.00000 108.0774  87.21043
## 46.14286 115.2815  93.64432
## 46.28571 120.8180  98.45879
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 165.9199 173.5556
## 44.57143 156.3500 165.4800
## 44.71429 144.1903 154.7796
## 44.85714 164.0082 175.7974
## 45.00000 164.0677 176.7291
## 45.14286 185.5465 198.8578
## 45.28571 191.5209 205.4285
## 45.42857 194.5303 210.2253
## 45.57143 189.7596 206.8793
## 45.71429 183.1441 201.7415
## 45.85714 181.2821 201.1636
## 46.00000 186.9147 207.7817
## 46.14286 197.0287 218.6659
## 46.28571 205.2933 227.6525
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 3.453526 3.453526 3.453526 3.453526 3.453526 3.453526 3.453526 3.453526
##  [9] 3.453526 3.453526 3.453526 3.453526 3.453526 3.453526
## 
## $三重県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -0.2449227 -2.202763
## 44.57143 -0.4496053 -2.515798
## 44.71429 -0.6440764 -2.813216
## 44.85714 -0.8297271 -3.097145
## 45.00000 -1.0076587 -3.369267
## 45.14286 -1.1787607 -3.630945
## 45.28571 -1.3437640 -3.883296
## 45.42857 -1.5032777 -4.127251
## 45.57143 -1.6578158 -4.363597
## 45.71429 -1.8078166 -4.593003
## 45.85714 -1.9536579 -4.816048
## 46.00000 -2.0956676 -5.033233
## 46.14286 -2.2341326 -5.244997
## 46.28571 -2.3693060 -5.451727
## 
## $三重県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%       95%
## 44.42857 7.151975  9.109816
## 44.57143 7.356658  9.422851
## 44.71429 7.551129  9.720269
## 44.85714 7.736780 10.004197
## 45.00000 7.914711 10.276320
## 45.14286 8.085813 10.537998
## 45.28571 8.250817 10.790349
## 45.42857 8.410330 11.034304
## 45.57143 8.564869 11.270650
## 45.71429 8.714869 11.500056
## 45.85714 8.860711 11.723101
## 46.00000 9.002720 11.940286
## 46.14286 9.141185 12.152050
## 46.28571 9.276359 12.358780
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 7.691420 4.279403 6.743976 5.688786 6.480042 6.154882 6.409462 6.309694
##  [9] 6.391798 6.361346 6.387894 6.378659 6.387268 6.384490
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%          95%
## 44.42857 3.777233  1.705187332
## 44.57143 0.303389 -1.801385802
## 44.71429 2.340169  0.008934445
## 44.85714 1.203004 -1.171625806
## 45.00000 1.832336 -0.628010194
## 45.14286 1.424788 -1.079173114
## 45.28571 1.577271 -0.980736265
## 45.42857 1.398310 -1.201619084
## 45.57143 1.396654 -1.247614849
## 45.71429 1.290122 -1.394421923
## 45.85714 1.240080 -1.485008099
## 46.00000 1.157350 -1.606643527
## 46.14286 1.093028 -1.709572784
## 46.28571 1.018938 -1.821413676
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%      95%
## 44.42857 11.605606 13.67765
## 44.57143  8.255417 10.36019
## 44.71429 11.147784 13.47902
## 44.85714 10.174568 12.54920
## 45.00000 11.127747 13.58809
## 45.14286 10.884977 13.38894
## 45.28571 11.241653 13.79966
## 45.42857 11.221078 13.82101
## 45.57143 11.386941 14.03121
## 45.71429 11.432570 14.11711
## 45.85714 11.535708 14.26080
## 46.00000 11.599968 14.36396
## 46.14286 11.681508 14.48411
## 46.28571 11.750042 14.59039
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 22.45250 20.56794 16.80859 18.31750 18.17835 18.36988 21.68664 20.03150
##  [9] 19.99999 18.17655 18.72278 19.46087 19.32481 20.50178
## 
## $京都府$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 16.099193 12.735957
## 44.57143 13.620937  9.943417
## 44.71429  9.632466  5.833654
## 44.85714 10.984157  7.102122
## 45.00000 10.707278  6.752330
## 45.14286 10.767942  6.743718
## 45.28571 13.957263  9.865578
## 45.42857 11.905750  7.604238
## 45.57143 11.658082  7.242141
## 45.71429  9.661234  5.153496
## 45.85714 10.047316  5.454802
## 46.00000 10.630859  5.956533
## 46.14286 10.343665  5.589334
## 46.28571 11.372213  6.539311
## 
## $京都府$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 28.80580 32.16904
## 44.57143 27.51494 31.19246
## 44.71429 23.98472 27.78353
## 44.85714 25.65084 29.53287
## 45.00000 25.64943 29.60438
## 45.14286 25.97182 29.99605
## 45.28571 29.41601 33.50770
## 45.42857 28.15725 32.45876
## 45.57143 28.34190 32.75785
## 45.71429 26.69187 31.19961
## 45.85714 27.39825 31.99076
## 46.00000 28.29088 32.96521
## 46.14286 28.30596 33.06029
## 46.28571 29.63135 34.46425
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 266.4114 238.5401 197.0315 286.1797 270.5087 276.2031 303.3945 313.0839
##  [9] 284.3115 244.9125 331.3623 331.3680 327.5330 350.9047
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 242.0321 229.1265
## 44.57143 211.5020 197.1889
## 44.71429 167.5735 151.9795
## 44.85714 254.4862 237.7087
## 45.00000 236.7272 218.8444
## 45.14286 240.4554 221.5317
## 45.28571 265.7832 245.8730
## 45.42857 270.0717 247.3025
## 45.57143 238.3875 214.0768
## 45.71429 196.2506 170.4905
## 45.85714 280.1085 252.9763
## 46.00000 277.6472 249.2091
## 46.14286 271.4537 241.7670
## 46.28571 292.5621 261.6774
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 290.7907 303.6964
## 44.57143 265.5782 279.8913
## 44.71429 226.4894 242.0835
## 44.85714 317.8733 334.6508
## 45.00000 304.2902 322.1730
## 45.14286 311.9508 330.8745
## 45.28571 341.0057 360.9159
## 45.42857 356.0960 378.8653
## 45.57143 330.2355 354.5462
## 45.71429 293.5745 319.3345
## 45.85714 382.6161 409.7482
## 46.00000 385.0888 413.5268
## 46.14286 383.6124 413.2990
## 46.28571 409.2474 440.1321
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 59.86323 60.35213 54.48538 74.04121 78.93017 84.30803 78.44128 73.97435
##  [9] 74.21337 71.34514 80.90591 83.29610 85.92531 83.05708
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 50.62476 45.73421
## 44.57143 50.53074 45.33160
## 44.71429 44.11377 38.62338
## 44.85714 63.14715 57.38018
## 45.00000 67.53759 61.50672
## 45.14286 72.43784 66.15414
## 45.28571 66.11197 59.58523
## 45.42857 59.35818 51.62085
## 45.57143 58.77763 50.60644
## 45.71429 55.13120 46.54806
## 45.85714 63.94945 54.97324
## 46.00000 65.62830 56.27553
## 46.14286 67.57372 57.85898
## 46.28571 64.04628 53.98257
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857  69.10171  73.99226
## 44.57143  70.17353  75.37266
## 44.71429  64.85699  70.34738
## 44.85714  84.93528  90.70225
## 45.00000  90.32276  96.35363
## 45.14286  96.17822 102.46192
## 45.28571  90.77059  97.29733
## 45.42857  88.59052  96.32786
## 45.57143  89.64911  97.82030
## 45.71429  87.55908  96.14222
## 45.85714  97.86237 106.83857
## 46.00000 100.96390 110.31667
## 46.14286 104.27690 113.99164
## 46.28571 102.06788 112.13159
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 16.73217 18.09593 16.35514 17.46404 18.66062 17.81700 18.32958 17.46928
##  [9] 17.86439 17.83428 17.89841 17.98522 17.81528 17.90207
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%       95%
## 44.42857 12.99819 11.021533
## 44.57143 14.15146 12.063383
## 44.71429 11.80812  9.401071
## 44.85714 12.82491 10.369107
## 45.00000 13.83726 11.283926
## 45.14286 12.70928 10.005412
## 45.28571 13.00753 10.190205
## 45.42857 11.84667  8.870242
## 45.57143 12.06620  8.996823
## 45.71429 11.82512  8.644068
## 45.85714 11.70576  8.427573
## 46.00000 11.60120  8.221702
## 46.14286 11.23567  7.752640
## 46.28571 11.14581  7.569270
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 20.46616 22.44282
## 44.57143 22.04040 24.12848
## 44.71429 20.90216 23.30920
## 44.85714 22.10317 24.55897
## 45.00000 23.48399 26.03732
## 45.14286 22.92473 25.62859
## 45.28571 23.65164 26.46896
## 45.42857 23.09189 26.06832
## 45.57143 23.66258 26.73196
## 45.71429 23.84344 27.02450
## 45.85714 24.09106 27.36925
## 46.00000 24.36925 27.74874
## 46.14286 24.39489 27.87793
## 46.28571 24.65832 28.23487
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 2.938907 3.662759 3.781629 3.668307 2.821283 2.958712 3.338256 3.351194
##  [9] 3.344375 3.344375 3.344375 3.344375 3.344375 3.344375
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%         95%
## 44.42857 1.0361834  0.02894244
## 44.57143 1.6112718  0.52528034
## 44.71429 1.6625906  0.54083973
## 44.85714 1.4838061  0.32740101
## 45.00000 0.5732236 -0.61682698
## 45.14286 0.6488430 -0.57392765
## 45.28571 0.9681890 -0.28644867
## 45.42857 0.9709982 -0.28900155
## 45.57143 0.9299270 -0.34820441
## 45.71429 0.8894596 -0.41009398
## 45.85714 0.8496486 -0.47097975
## 46.00000 0.8104629 -0.53090905
## 46.14286 0.7718741 -0.58992558
## 46.28571 0.7338556 -0.64806982
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 4.841630 5.848871
## 44.57143 5.714245 6.800237
## 44.71429 5.900666 7.022417
## 44.85714 5.852809 7.009214
## 45.00000 5.069342 6.259392
## 45.14286 5.268580 6.491351
## 45.28571 5.708323 6.962961
## 45.42857 5.731391 6.991390
## 45.57143 5.758823 7.036954
## 45.71429 5.799290 7.098844
## 45.85714 5.839101 7.159729
## 46.00000 5.878287 7.219659
## 46.14286 5.916876 7.278675
## 46.28571 5.954894 7.336820
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.6253698 0.5726303 0.5309663 0.4980519 0.4720497 0.4515082 0.4352804
##  [8] 0.4224606 0.4123329 0.4043322 0.3980116 0.3930184 0.3890738 0.3859576
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%        95%
## 44.42857 -0.3194814 -0.8196554
## 44.57143 -0.3871014 -0.8951526
## 44.71429 -0.4386307 -0.9519043
## 44.85714 -0.4782186 -0.9950250
## 45.00000 -0.5088313 -1.0280783
## 45.14286 -0.5326306 -1.0536022
## 45.28571 -0.5512169 -1.0734370
## 45.42857 -0.5657890 -1.0889367
## 45.57143 -0.5772543 -1.1011101
## 45.71429 -0.5863052 -1.1107169
## 45.85714 -0.5934735 -1.1183340
## 46.00000 -0.5991699 -1.1244027
## 46.14286 -0.6037132 -1.1292628
## 46.28571 -0.6073514 -1.1331774
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.570221 2.070395
## 44.57143 1.532362 2.040413
## 44.71429 1.500563 2.013837
## 44.85714 1.474322 1.991129
## 45.00000 1.452931 1.972178
## 45.14286 1.435647 1.956619
## 45.28571 1.421778 1.943998
## 45.42857 1.410710 1.933858
## 45.57143 1.401920 1.925776
## 45.71429 1.394970 1.919381
## 45.85714 1.389497 1.914357
## 46.00000 1.385207 1.910439
## 46.14286 1.381861 1.907410
## 46.28571 1.379267 1.905093
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158
##  [8] 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158 0.4638158
## 
## $島根県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -6.329338 -9.925416
## 44.57143 -6.329338 -9.925416
## 44.71429 -6.329338 -9.925416
## 44.85714 -6.329338 -9.925416
## 45.00000 -6.329338 -9.925416
## 45.14286 -6.329338 -9.925416
## 45.28571 -6.329338 -9.925416
## 45.42857 -6.329338 -9.925416
## 45.57143 -6.329338 -9.925416
## 45.71429 -6.329338 -9.925416
## 45.85714 -6.329338 -9.925416
## 46.00000 -6.329338 -9.925416
## 46.14286 -6.329338 -9.925416
## 46.28571 -6.329338 -9.925416
## 
## $島根県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##              80%      95%
## 44.42857 7.25697 10.85305
## 44.57143 7.25697 10.85305
## 44.71429 7.25697 10.85305
## 44.85714 7.25697 10.85305
## 45.00000 7.25697 10.85305
## 45.14286 7.25697 10.85305
## 45.28571 7.25697 10.85305
## 45.42857 7.25697 10.85305
## 45.57143 7.25697 10.85305
## 45.71429 7.25697 10.85305
## 45.85714 7.25697 10.85305
## 46.00000 7.25697 10.85305
## 46.14286 7.25697 10.85305
## 46.28571 7.25697 10.85305
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 9.380398 8.832785 8.537639 6.915806 8.235033 8.620941 8.582659 8.135755
##  [9] 8.228821 8.602231 8.756594 7.933466 7.804291 7.305831
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 6.754638 5.364644
## 44.57143 5.901077 4.349125
## 44.71429 5.490969 3.878160
## 44.85714 3.798810 2.148772
## 45.00000 5.061367 3.381330
## 45.14286 5.395599 3.688207
## 45.28571 5.307837 3.574251
## 45.42857 4.754776 2.964994
## 45.57143 4.776044 2.948255
## 45.71429 5.088781 3.228873
## 45.85714 5.186748 3.296986
## 46.00000 4.309229 2.390673
## 46.14286 4.126863 2.180149
## 46.28571 3.576110 1.601715
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 12.00616 13.39615
## 44.57143 11.76449 13.31645
## 44.71429 11.58431 13.19712
## 44.85714 10.03280 11.68284
## 45.00000 11.40870 13.08874
## 45.14286 11.84628 13.55368
## 45.28571 11.85748 13.59107
## 45.42857 11.51673 13.30652
## 45.57143 11.68160 13.50939
## 45.71429 12.11568 13.97559
## 45.85714 12.32644 14.21620
## 46.00000 11.55770 13.47626
## 46.14286 11.48172 13.42843
## 46.28571 11.03555 13.00995
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 2.450610 2.364071 2.673973 2.654067 2.493853 2.538447 2.613988 2.574280
##  [9] 2.542630 2.570423 2.581314 2.564253 2.562098 2.571608
## 
## $広島県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.066412 -2.928211
## 44.57143 -1.537950 -3.603555
## 44.71429 -1.418965 -3.585636
## 44.85714 -2.041553 -4.527264
## 45.00000 -2.741972 -5.513651
## 45.14286 -2.981292 -5.903265
## 45.28571 -3.210849 -6.294331
## 45.42857 -3.630624 -6.915301
## 45.57143 -3.982581 -7.436820
## 45.71429 -4.224984 -7.822255
## 45.85714 -4.498417 -8.246200
## 46.00000 -4.801346 -8.700459
## 46.14286 -5.063718 -9.100582
## 46.28571 -5.302772 -9.471218
## 
## $広島県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857  5.967631  7.829430
## 44.57143  6.266092  8.331697
## 44.71429  6.766911  8.933582
## 44.85714  7.349686  9.835397
## 45.00000  7.729679 10.501357
## 45.14286  8.058185 10.980159
## 45.28571  8.438825 11.522307
## 45.42857  8.779183 12.063861
## 45.57143  9.067842 12.522080
## 45.71429  9.365830 12.963101
## 45.85714  9.661044 13.408827
## 46.00000  9.929852 13.828965
## 46.14286 10.187914 14.224777
## 46.28571 10.445989 14.614434
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 5.321074 5.321074 5.321074 5.321074 5.321074 5.321074 5.321074 5.321074
##  [9] 5.321074 5.321074 5.321074 5.321074 5.321074 5.321074
## 
## $山口県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%       95%
## 44.42857 3.208137 2.0896162
## 44.57143 3.125936 1.9639001
## 44.71429 3.046704 1.8427248
## 44.85714 2.970140 1.7256311
## 45.00000 2.895993 1.6122323
## 45.14286 2.824046 1.5021994
## 45.28571 2.754115 1.3952493
## 45.42857 2.686040 1.2911365
## 45.57143 2.619679 1.1896465
## 45.71429 2.554910 1.0905906
## 45.85714 2.491623 0.9938016
## 46.00000 2.429721 0.8991306
## 46.14286 2.369117 0.8064444
## 46.28571 2.309732 0.7156232
## 
## $山口県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 7.434012 8.552533
## 44.57143 7.516213 8.678249
## 44.71429 7.595445 8.799424
## 44.85714 7.672009 8.916518
## 45.00000 7.746156 9.029917
## 45.14286 7.818103 9.139950
## 45.28571 7.888034 9.246900
## 45.42857 7.956109 9.351013
## 45.57143 8.022470 9.452502
## 45.71429 8.087239 9.551558
## 45.85714 8.150526 9.648347
## 46.00000 8.212428 9.743018
## 46.14286 8.273032 9.835705
## 46.28571 8.332417 9.926526
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] -0.2390366  0.3118990  0.4821410  0.5971257  0.2838376  0.2788330
##  [7] -0.2613762  0.4525828  0.3915614  0.2457528  0.1138983  0.3976689
## [13]  0.3992576  0.4820118
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.970186 -2.886601
## 44.57143 -1.502653 -2.463219
## 44.71429 -1.351265 -2.321812
## 44.85714 -1.254943 -2.235369
## 45.00000 -1.586707 -2.576914
## 45.14286 -1.610007 -2.609899
## 45.28571 -2.168336 -3.177820
## 45.42857 -1.551015 -2.611656
## 45.57143 -1.646457 -2.725319
## 45.71429 -1.816566 -2.908292
## 45.85714 -1.972438 -3.076878
## 46.00000 -1.712412 -2.829421
## 46.14286 -1.734303 -2.863741
## 46.28571 -1.674772 -2.816505
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.492113 2.408528
## 44.57143 2.126451 3.087017
## 44.71429 2.315547 3.286094
## 44.85714 2.449194 3.429620
## 45.00000 2.154382 3.144589
## 45.14286 2.167673 3.167565
## 45.28571 1.645584 2.655068
## 45.42857 2.456181 3.516821
## 45.57143 2.429580 3.508442
## 45.71429 2.308072 3.399798
## 45.85714 2.200235 3.304675
## 46.00000 2.507749 3.624758
## 46.14286 2.532818 3.662256
## 46.28571 2.638796 3.780529
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.3614465 0.6316022 0.6316022 0.6316022 0.6316022 0.6316022 0.6316022
##  [8] 0.6316022 0.6316022 0.6316022 0.6316022 0.6316022 0.6316022 0.6316022
## 
## $香川県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -1.0096384 -1.735447
## 44.57143 -0.8010429 -1.559439
## 44.71429 -0.8051110 -1.565661
## 44.85714 -0.8091677 -1.571865
## 45.00000 -0.8132130 -1.578052
## 45.14286 -0.8172469 -1.584221
## 45.28571 -0.8212697 -1.590374
## 45.42857 -0.8252814 -1.596509
## 45.57143 -0.8292821 -1.602627
## 45.71429 -0.8332718 -1.608729
## 45.85714 -0.8372507 -1.614814
## 46.00000 -0.8412189 -1.620883
## 46.14286 -0.8451763 -1.626936
## 46.28571 -0.8491233 -1.632972
## 
## $香川県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.732531 2.458340
## 44.57143 2.064247 2.822644
## 44.71429 2.068315 2.828865
## 44.85714 2.072372 2.835070
## 45.00000 2.076417 2.841256
## 45.14286 2.080451 2.847426
## 45.28571 2.084474 2.853578
## 45.42857 2.088486 2.859713
## 45.57143 2.092486 2.865832
## 45.71429 2.096476 2.871934
## 45.85714 2.100455 2.878019
## 46.00000 2.104423 2.884088
## 46.14286 2.108381 2.890140
## 46.28571 2.112328 2.896176
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 1.2600148 0.3529385 0.6679568 0.1899741 0.5676428 0.6399705 0.7428348
##  [8] 0.3562783 0.5476973 0.3111681 0.5096945 0.3182187 0.4931993 0.3300007
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857 -0.3861089 -1.257514
## 44.57143 -1.3416889 -2.238770
## 44.71429 -1.0544228 -1.966196
## 44.85714 -1.5329936 -2.445078
## 45.00000 -1.1599774 -2.074524
## 45.14286 -1.0898645 -2.005584
## 45.28571 -0.9894317 -1.906438
## 45.42857 -1.3789941 -2.297592
## 45.57143 -1.1918324 -2.112684
## 45.71429 -1.4287425 -2.349796
## 45.85714 -1.2312762 -2.152890
## 46.00000 -1.4233812 -2.345328
## 46.14286 -1.2490286 -2.171308
## 46.28571 -1.4127380 -2.335288
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.906139 3.777544
## 44.57143 2.047566 2.944647
## 44.71429 2.390336 3.302109
## 44.85714 1.912942 2.825026
## 45.00000 2.295263 3.209810
## 45.14286 2.369806 3.285525
## 45.28571 2.475101 3.392108
## 45.42857 2.091551 3.010149
## 45.57143 2.287227 3.208078
## 45.71429 2.051079 2.972132
## 45.85714 2.250665 3.172279
## 46.00000 2.059819 2.981766
## 46.14286 2.235427 3.157707
## 46.28571 2.072739 2.995290
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.1498205 0.1725030 0.1934586 0.2128187 0.2307049 0.2472294 0.2624958
##  [8] 0.2765999 0.2896303 0.3016686 0.3127903 0.3230654 0.3325581 0.3413281
## 
## $高知県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.550524 -2.450632
## 44.57143 -1.549597 -2.461221
## 44.71429 -1.546995 -2.468336
## 44.85714 -1.543149 -2.472702
## 45.00000 -1.538396 -2.474902
## 45.14286 -1.533006 -2.475405
## 45.28571 -1.527187 -2.474588
## 45.42857 -1.521108 -2.472757
## 45.57143 -1.514899 -2.470159
## 45.71429 -1.508662 -2.466994
## 45.85714 -1.502478 -2.463423
## 46.00000 -1.496406 -2.459577
## 46.14286 -1.490494 -2.455559
## 46.28571 -1.484774 -2.451454
## 
## $高知県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.850165 2.750273
## 44.57143 1.894603 2.806227
## 44.71429 1.933912 2.855253
## 44.85714 1.968786 2.898339
## 45.00000 1.999806 2.936312
## 45.14286 2.027464 2.969864
## 45.28571 2.052179 2.999580
## 45.42857 2.074308 3.025957
## 45.57143 2.094159 3.049419
## 45.71429 2.111999 3.070331
## 45.85714 2.128059 3.089004
## 46.00000 2.142537 3.105707
## 46.14286 2.155610 3.120676
## 46.28571 2.167430 3.134111
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 13.33963 12.67237 14.45140 19.35712 19.94395 17.90987 17.11662 14.92710
##  [9] 14.74720 16.13915 19.73165 19.93509 18.87181 17.30216
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                  80%        95%
## 44.42857  -0.2203831  -7.398621
## 44.57143  -3.2587693 -11.692205
## 44.71429  -2.7615255 -11.873496
## 44.85714   1.4442635  -8.038231
## 45.00000   1.4191021  -8.387361
## 45.14286  -1.5100173 -11.790285
## 45.28571  -3.6550909 -14.650971
## 45.42857  -8.4460670 -20.819074
## 45.57143 -10.4637185 -23.809574
## 45.71429 -10.2944972 -24.287626
## 45.85714  -7.4993966 -21.914642
## 46.00000  -7.9899938 -22.772640
## 46.14286  -9.9202145 -25.161792
## 46.28571 -12.6686750 -28.534278
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 26.89965 34.07788
## 44.57143 28.60351 37.03695
## 44.71429 31.66432 40.77629
## 44.85714 37.26998 46.75248
## 45.00000 38.46880 48.27527
## 45.14286 37.32976 47.61003
## 45.28571 37.88833 48.88421
## 45.42857 38.30027 50.67327
## 45.57143 39.95812 53.30398
## 45.71429 42.57280 56.56593
## 45.85714 46.96270 61.37794
## 46.00000 47.86017 62.64282
## 46.14286 47.66384 62.90542
## 46.28571 47.27300 63.13860
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095
##  [8] 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095 0.9985095
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.000088 -2.058082
## 44.57143 -1.075772 -2.173830
## 44.71429 -1.148790 -2.285501
## 44.85714 -1.219405 -2.393498
## 45.00000 -1.287840 -2.498160
## 45.14286 -1.354285 -2.599780
## 45.28571 -1.418905 -2.698607
## 45.42857 -1.481842 -2.794861
## 45.57143 -1.543221 -2.888732
## 45.71429 -1.603152 -2.980389
## 45.85714 -1.661734 -3.069982
## 46.00000 -1.719053 -3.157643
## 46.14286 -1.775187 -3.243494
## 46.28571 -1.830208 -3.327641
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.997107 4.055101
## 44.57143 3.072791 4.170849
## 44.71429 3.145809 4.282520
## 44.85714 3.216424 4.390517
## 45.00000 3.284859 4.495179
## 45.14286 3.351304 4.596799
## 45.28571 3.415924 4.695626
## 45.42857 3.478861 4.791880
## 45.57143 3.540240 4.885751
## 45.71429 3.600171 4.977408
## 45.85714 3.658753 5.067001
## 46.00000 3.716072 5.154662
## 46.14286 3.772206 5.240513
## 46.28571 3.827227 5.324660
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.1678406 0.2938401 0.2064578 0.2511659 0.2282657 0.3271986 0.6873682
##  [8] 0.2305234 0.2915630 0.2756545 0.2838072 0.2796438 0.2572673 0.3380083
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.967878 -3.098458
## 44.57143 -2.082778 -3.340884
## 44.71429 -2.659724 -4.176989
## 44.85714 -2.902585 -4.572080
## 45.00000 -3.248004 -5.088230
## 45.14286 -3.415660 -5.397010
## 45.28571 -3.317821 -5.438040
## 45.42857 -4.166185 -6.493662
## 45.57143 -4.392409 -6.871954
## 45.71429 -4.713014 -7.353856
## 45.85714 -4.975291 -7.759289
## 46.00000 -5.244737 -8.169167
## 46.14286 -5.516252 -8.572568
## 46.28571 -5.676285 -8.860060
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.303559 3.434140
## 44.57143 2.670459 3.928564
## 44.71429 3.072640 4.589905
## 44.85714 3.404917 5.074412
## 45.00000 3.704535 5.544761
## 45.14286 4.070058 6.051407
## 45.28571 4.692557 6.812776
## 45.42857 4.627232 6.954709
## 45.57143 4.975535 7.455080
## 45.71429 5.264323 7.905165
## 45.85714 5.542905 8.326904
## 46.00000 5.804024 8.728454
## 46.14286 6.030786 9.087103
## 46.28571 6.352302 9.536076
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 7.361192 7.358439 7.545754 7.545754 7.545754 7.545754 7.545754 7.545754
##  [9] 7.545754 7.545754 7.545754 7.545754 7.545754 7.545754
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                  80%        95%
## 44.42857  3.16282273  0.9403404
## 44.57143  1.76790472 -1.1915455
## 44.71429  1.79912402 -1.2429580
## 44.85714  1.46674563 -1.7512869
## 45.00000  1.15162159 -2.2332275
## 45.14286  0.85131482 -2.6925071
## 45.28571  0.56391309 -3.1320501
## 45.42857  0.28788316 -3.5542014
## 45.57143  0.02197332 -3.9608755
## 45.71429 -0.23485410 -4.3536591
## 45.85714 -0.48347068 -4.7338853
## 46.00000 -0.72461694 -5.1026867
## 46.14286 -0.95892836 -5.4610351
## 46.28571 -1.18695512 -5.8097720
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 11.55956 13.78204
## 44.57143 12.94897 15.90842
## 44.71429 13.29238 16.33447
## 44.85714 13.62476 16.84279
## 45.00000 13.93989 17.32473
## 45.14286 14.24019 17.78401
## 45.28571 14.52759 18.22356
## 45.42857 14.80362 18.64571
## 45.57143 15.06953 19.05238
## 45.71429 15.32636 19.44517
## 45.85714 15.57498 19.82539
## 46.00000 15.81612 20.19419
## 46.14286 16.05044 20.55254
## 46.28571 16.27846 20.90128
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 0.27838636 0.05000969 0.16260918 0.36957661 0.37674708 0.38353892
##  [7] 0.38997213 0.39606564 0.40183739 0.40730437 0.41248267 0.41738755
## [13] 0.42203342 0.42643398
## 
## $大分県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -1.097401 -1.825699
## 44.57143 -1.535633 -2.375021
## 44.71429 -1.591234 -2.519663
## 44.85714 -1.448120 -2.410350
## 45.00000 -1.450404 -2.417639
## 45.14286 -1.452053 -2.423757
## 45.28571 -1.453160 -2.428855
## 45.42857 -1.453805 -2.433068
## 45.57143 -1.454059 -2.436511
## 45.71429 -1.453981 -2.439285
## 45.85714 -1.453624 -2.441481
## 46.00000 -1.453034 -2.443176
## 46.14286 -1.452252 -2.444438
## 46.28571 -1.451310 -2.445328
## 
## $大分県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 1.654174 2.382471
## 44.57143 1.635652 2.475040
## 44.71429 1.916453 2.844881
## 44.85714 2.187273 3.149503
## 45.00000 2.203898 3.171133
## 45.14286 2.219131 3.190835
## 45.28571 2.233104 3.208799
## 45.42857 2.245937 3.225199
## 45.57143 2.257733 3.240185
## 45.71429 2.268589 3.253894
## 45.85714 2.278589 3.266446
## 46.00000 2.287809 3.277951
## 46.14286 2.296318 3.288505
## 46.28571 2.304178 3.298196
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] -0.01540328 -0.01408748 -0.01408748 -0.01408748 -0.01408748 -0.01408748
##  [7] -0.01408748 -0.01408748 -0.01408748 -0.01408748 -0.01408748 -0.01408748
## [13] -0.01408748 -0.01408748
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                80%       95%
## 44.42857 -2.710222 -4.136773
## 44.57143 -2.823440 -4.310622
## 44.71429 -3.148923 -4.808405
## 44.85714 -3.443654 -5.259157
## 45.00000 -3.714987 -5.674125
## 45.14286 -3.967743 -6.060681
## 45.28571 -4.205283 -6.423968
## 45.42857 -4.430065 -6.767742
## 45.57143 -4.643946 -7.094844
## 45.71429 -4.848373 -7.407489
## 45.85714 -5.044500 -7.707439
## 46.00000 -5.233262 -7.996126
## 46.14286 -5.415431 -8.274730
## 46.28571 -5.591654 -8.544239
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 2.679415 4.105966
## 44.57143 2.795265 4.282447
## 44.71429 3.120748 4.780230
## 44.85714 3.415479 5.230982
## 45.00000 3.686812 5.645950
## 45.14286 3.939568 6.032506
## 45.28571 4.177108 6.395793
## 45.42857 4.401890 6.739567
## 45.57143 4.615771 7.066669
## 45.71429 4.820198 7.379314
## 45.85714 5.016325 7.679264
## 46.00000 5.205087 7.967951
## 46.14286 5.387256 8.246555
## 46.28571 5.563479 8.516064
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 4.401976 4.042681 3.826815 3.697122 3.619202 3.572388 3.544262 3.527363
##  [9] 3.517211 3.511111 3.507446 3.505244 3.503922 3.503127
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##                 80%       95%
## 44.42857  0.4200927 -1.687789
## 44.57143 -0.6692692 -3.163625
## 44.71429 -1.1625913 -3.803823
## 44.85714 -1.4148551 -4.120972
## 45.00000 -1.5543060 -4.292996
## 45.14286 -1.6363723 -4.393723
## 45.28571 -1.6875408 -4.457090
## 45.42857 -1.7214066 -4.499938
## 45.57143 -1.7453236 -4.531141
## 45.71429 -1.7634199 -4.555588
## 45.85714 -1.7780717 -4.576056
## 46.00000 -1.7906689 -4.594156
## 46.14286 -1.8020330 -4.610836
## 46.28571 -1.8126524 -4.626656
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 8.383858 10.49174
## 44.57143 8.754630 11.24899
## 44.71429 8.816221 11.45745
## 44.85714 8.809099 11.51522
## 45.00000 8.792711 11.53140
## 45.14286 8.781148 11.53850
## 45.28571 8.776064 11.54561
## 45.42857 8.776133 11.55466
## 45.57143 8.779745 11.56556
## 45.71429 8.785642 11.57781
## 45.85714 8.792964 11.59095
## 46.00000 8.801158 11.60464
## 46.14286 8.809876 11.61868
## 46.28571 8.818906 11.63291
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##  [1] 38.97211 38.61653 36.52152 37.78856 40.59140 37.20556 43.33177 40.43161
##  [9] 40.43161 40.43161 40.43161 40.43161 40.43161 40.43161
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%       95%
## 44.42857 27.17029 20.922777
## 44.57143 25.72393 18.899004
## 44.71429 22.62350 15.266334
## 44.85714 22.95311 15.099689
## 45.00000 24.87432 16.554198
## 45.14286 20.65374 11.891742
## 45.28571 25.98534 16.802698
## 45.42857 21.34217 11.236828
## 45.57143 20.29401  9.633812
## 45.71429 19.29778  8.110202
## 45.85714 18.34644  6.655250
## 46.00000 17.43442  5.260436
## 46.14286 16.55721  3.918865
## 46.28571 15.71111  2.624870
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 4) 
## End = c(46, 3) 
## Frequency = 7 
##               80%      95%
## 44.42857 50.77394 57.02145
## 44.57143 51.50912 58.33405
## 44.71429 50.41954 57.77670
## 44.85714 52.62402 60.47744
## 45.00000 56.30849 64.62861
## 45.14286 53.75738 62.51938
## 45.28571 60.67820 69.86084
## 45.42857 59.52106 69.62640
## 45.57143 60.56921 71.22942
## 45.71429 61.56545 72.75303
## 45.85714 62.51679 74.20798
## 46.00000 63.42881 75.60279
## 46.14286 64.30602 76.94436
## 46.28571 65.15212 78.23836